Journal of Emerging Trends in Engineering and Applied Sciences: Research Areas

Article Title: FUTACOVNET: A Deep CNN Network for Detection of Coronavirus (Covid-19) Using Chest X-Ray Images
by Olatubosun Olabode, Kolawole Akintola, Adeyemi Ebenezer, Olufemi Akinyede Rapheal, Afeni Babajide, Racheal Akinbo and Adekemi Iyabo Olabode

Abstract:
In December 2019, WHO declared COVID-19 a as morbidity and mortality rates continue to soar high with a global cumulative case of 460,280,168 and cumulative mortality of 6,050,018. The standard clinical golden tool mostly used for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR). It is adjudged to be very expensive, less-sensitive, not readily available in hospitals and most significantly, requires the services of a specialized medical expert. X-ray imaging is an easily accessible tool that can be an excellent alternative tool in COVID-19 diagnosis. This paper proposed a technique to automatically predict the presence of COVID-19 pneumonia from digital chest X-ray images using deep learning. Any technological tool that can help in the effective screening of the COVID-19 infection with high level of accuracy is highly required. In this research, the use of transfer learning approach in the rapid and accurate diagnosis of COVID-19 from chest X-ray images is carried out. A new CNN architecture that is trainable optimally while maximizing the detection accuracy is developed. A database was created by combining several public databases and also by collecting images from National Hospital, Abuja. The database contains a mixture of 3616 COVID-19 and 10,192 normal chest X-ray images. The X-ray images were used to train and validate the deep Convolutional Neural Network (CNN) model. The trained network was then used to classify the normal and COVID-19 patients. The proposed CNN classification accuracy, precision, recall and F1-Score of the model are 96.5%, 96%, 96% and 96% respectively. The model was then compared with the state of the art CNN models and it outperformed all of them, The high accuracy of this model can significantly improve the speed and accuracy of COVID-19 diagnosis in our local hospitals. This would be extremely useful in this pandemic where facilities and human resources for early diagnosis and management are lacking.

Keywords: X-Rays, COVID-19 Pneumonia, Deep Learning, Transfer Learning, Pandemic And Diagnosis

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